Even though the context obtained for this meme pertains to its content (see Fig. REF ), it does not relate to global racial prejudice, which is key to ascertaining it as a harmfully targeting meme. Moreover, besides context, visuals and the message embedded within the meme do not convey definite harm when considered in isolation. This error can be inferred clearly from the embedded-harmfulness, contextualised-visuals, and the visuals being attended by DISARM as depicted in Fig. REF , Fig. REF , and Fig. REF , respectively. On the other hand, as shown in the visual attention plot for ViLBERT in Fig. REF , the field of view that is being attended encompasses the visuals of Kamala Harris, who is the person of colour that i sbeing primarily targeted by the meme.
Besides the distinct attention on the primary target-candidate within the meme, ViLBERT could have leveraged the pre-training it received from Conceptual Captions (CC) [1]}, a dataset known for its diverse coverage of complex textual descriptions. This essentially highlights the importance of making use of multimodal pre-training using the dataset that is not as generic as MS COCO [2]}, but facilitates modelling of the complex real-world multimodal information, especially for tasks related to memes.
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